Digital signal processing, and the digital signal processors (DSPs) within which such processing occurs, are known. DSPs are typically used whenever significant numbers of sample parameters must be collected and/or communicated. DSPs in such an environment are used to control sampling and encoding of a signal for storage or transmission to a destination. Although the most familiar uses of such systems lie in the area of voice processing for communications systems, significant applications for such techniques exist in other areas such as geologic surveying or other multi-dimensional data gathering activities.
Use of DSPs in the context of signal gathering and transmission is often limited to a single input signal. Such single input format is convenient because of the complexity of the multi-input data encoding within the transmission channel and also because of the computational complexity of multichannel signal processing within a DSP.
Within the context of a single transmission channel a DSP provides considerable flexibility in adapting to the constraints of the channel. Data samples gathered at a first sampling rate may be reconstructed within the DSP at a second sampling rate without loss of information under the well-known sampling theorem (see pgs. 20-21 of Multirate Digital Signal Processing by Crochiere and Rabiner, Prentice-Hall Inc., 1983).
Where the sampling rate is increased, the values of the increased sampling rate are generated through a process generally referred to as interpolation. As the sampling rate, n, increases (n approaches infinity) a continuous signal (analogous to the originally sampled signal) is generated.
Where the sampling rate is decreased, the process is referred to as decimation. The only limit on decimation is that the sampling rate may not be decreased below twice the highest frequency present in the sampled signal, all in accordance with the familiar Nyquist theorem.
In addition to decimation and interpolation, a bandwidth of a sampled signal may be translated in frequency from a first frequency to a second frequency. As an example, a sampled signal may be frequency translated from a zero-radio frequency (zero-rf) baseband to an appropriate spectrum for transmission through a medium to a destination (see Crochiere and Rabiner, pgs. 48-56).
At a destination, the bandwidth containing the encoded sample stream may, again, be frequency translated to the baseband. At the baseband the sampled information may be recovered directly, or the sampled information may be decimated or interpolated to a more convenient form for the recovery system.
Data streams from multiple sampling sources may also be encoded within a DSP under a multi-channel encoding scheme, translated to a transmission spectrum and transmitted to a destination for decoding. Such a multi-channel encoding scheme is described in Crochiere and Rabiner (Chapter 7) in which a K-channel synthesizer (encoder) and a K-channel analyzer (decoder) are described.
Under the system taught by Crocheire and Rabiner, information is transferred under a channel stacking arrangement that places respective channels, spectrally, in close proximity. Two types of channel stacking are described (odd and even). Channel stacking in either case results in channel spacings in the order of no more than two channels.
While the system taught by Crochiere and Rabiner may be effective as described, such systems are limited in their ability to transcode spectrally diverse signals. Because of the importance of information exchange, a need exists for a method of decoding selected channels that may be widely separated within a transmission spectrum.